MADS-box protein PpDAM6 regulates chilling requirement-mediated dormancy and bud break in peach.

Bud dormancy is crucial for winter survival and is characterized by the inability of the bud meristem to respond to growth-promotive signals before the chilling requirement (CR) is met. However, our understanding of the genetic mechanism regulating CR and bud dormancy remains limited. This study identified PpDAM6 (DORMANCY-ASSOCIATED MADS-box) as a key gene for CR using a genome-wide association study analysis based on structural variations in 345 peach (Prunus persica (L.) Batsch) accessions. The function of PpDAM6 in CR regulation was demonstrated by transiently silencing the gene in peach buds and stably overexpressing the gene in transgenic apple (Malus × domestica) plants. The results showed an evolutionarily conserved function of PpDAM6 in regulating bud dormancy release, followed by vegetative growth and flowering, in peach and apple. The 30-bp deletion in the PpDAM6 promoter was substantially associated with reducing PpDAM6 expression in low-CR accessions. A PCR marker based on the 30-bp indel was developed to distinguish peach plants with non-low and low CR. Modification of the H3K27me3 marker at the PpDAM6 locus showed no apparent change across the dormancy process in low- and non-low- CR cultivars. Additionally, H3K27me3 modification occurred earlier in low-CR cultivars on a genome-wide scale. PpDAM6 could mediate cell-cell communication by inducing the expression of the downstream genes PpNCED1 (9-cis-epoxycarotenoid dioxygenase 1), encoding a key enzyme for ABA biosynthesis, and CALS (CALLOSE SYNTHASE), encoding callose synthase. We shed light on a gene regulatory network formed by PpDAM6-containing complexes that mediate CR underlying dormancy and bud break in peach. A better understanding of the genetic basis for natural variations of CR can help breeders develop cultivars with different CR for growing in different geographical regions.

Haplotype-resolved genome of a heterozygous wild peach reveals the PdaWRKY4-PdaCYP716A1 module mediates resistance to aphids by regulating betulin biosynthesis.

Wild species of domesticated crops provide valuable genetic resources for resistance breeding. Prunus davidiana, a wild relative of peach with high heterozygosity and diverse stress tolerance, exhibits high resistance against aphids. However, the highly heterozygous genome of P. davidiana makes determining the underlying factors influencing resistance traits challenging. Here, we present the 501.7 Mb haplotype-resolved genome assembly of P. davidiana. Genomic comparisons of the two haplotypes revealed 18,152 structural variations, 2,699 Pda_hap1-specific and 2,702 Pda_hap2-specific genes, and 1,118 allele-specific expressed genes. Genome composition indicated 4.1% of the P. davidiana genome was non-peach origin, out of which 94.5% was derived from almond. Based on the haplotype genome, the aphid resistance quantitative trait locus (QTL) was mapped at the end of Pda03. From the aphid resistance QTL, PdaWRKY4 was identified as the major dominant gene, with a 9-bp deletion in its promoter of the resistant phenotype. Specifically, PdaWRKY4 regulates aphid resistance by promoting PdaCYP716A1-mediated anti-aphid metabolite betulin biosynthesis. Moreover, we employed a genome design to develop a breeding workflow for rapidly and precisely producing aphid-resistant peaches. In conclusion, this study identifies a novel aphid resistance gene and provides insights into genome design for the development of resistant fruit cultivars.

The occurrence of and risk factors for developing acute critical illness during quarantine as a response to the COVID-19 pandemic.

BACKGROUND/PURPOSE: Early detection and timely quarantine measures are necessary to control disease spread and prevent nosocomial outbreaks of Coronavirus disease 2019 (COVID-19). In this study, we aimed to investigate the impact of a quarantine strategy on patient safety and quality of care. METHODS: This retrospective cohort study enrolled patients admitted to the quarantine ward in a tertiary hospital in southern Taiwan. The incidence and causes of acute critical illness, including clinical deterioration and unexpected complications during the quarantine period, were reviewed. Further investigation was performed to identify risk factors for acute critical illness during quarantine. RESULTS: Of 320 patients admitted to the quarantine ward, more than two-thirds were elderly, and 37.8% were bedridden. During the quarantine period, 68 (21.2%) developed acute critical illness, which more commonly occurred among patients older than 80 years and with a bedridden status, nasogastric tube feeding, or dyspnea symptoms. Bedridden status was an independent predictor of acute critical illness. Through optimization of sampling for COVID-19 and laboratory schedules, both the duration of quarantine and the proportion of acute critical illness among bedridden patients during quarantine exhibited a decreasing trend. There was no COVID-19 nosocomial transmission during the study period. CONCLUSION: The quarantine ward is a key measure to prevent nosocomial transmission of COVID-19 but may carry a potential negative impact on patient care and safety. For patients with multiple comorbidities and a bedridden status, healthcare workers should remain alert to rapid deterioration and unexpected adverse events during quarantine.

SABA prescriptions and asthma management practices in patients treated by specialists in Taiwan: Results from the SABINA III study.

PURPOSE: Limited data exist on asthma medication patterns in Taiwan. The objectives of the SABINA III cross-sectional study in Taiwan were thus, to describe patient demographics and clinical features and estimate short-acting ß2-agonist (SABA) and inhaled corticosteroids (ICS) prescriptions per patient. METHODS: Patients (≥18 years) with asthma were classified by investigator-defined asthma severity per the 2017 Global Initiative for Asthma (GINA) recommendations. Data on asthma symptom control (per GINA 2017 recommendations), severe exacerbation history, and prescribed treatments in the 12 months before study visit were collected using electronic case-report forms. Analyses were descriptive. RESULTS: Overall, all 294 analyzed patients (mean [SD] age, 57.9 [15.6] years; female, 69%) were enrolled by specialists and had fully reimbursed healthcare. Most patients were classified with moderate-to-severe asthma (93.2%; GINA steps 3-5), were obese (53.4%) and nonsmokers (79.6%), reported high school or university and/or postgraduate education (61.9%), and had ≤2 comorbidities (89.1%). Mean (SD) asthma duration was 8.3 (10.0) years, with 37.8% of patients experiencing ≥1 severe exacerbation 12 months before the study visit. Overall, 62.2%, 26.2%, and 11.6% of patients had well-controlled, partly controlled, and uncontrolled asthma, respectively. Crucially, 19.3% of patients were prescribed ≥3 SABA canisters in the preceding 12 months (overprescription). ICS, ICS + long-acting ß2-agonist fixed-dose combination, and oral corticosteroid bursts were prescribed to 6.5%, 97.3%, and 31.6% of patients, respectively. CONCLUSION: Despite treatment by specialists and fully reimbursed healthcare, findings indicate room for improvement in asthma control and SABA prescription practices in Taiwan, emphasizing the need to adhere to latest evidence-based guidelines.

Dry Wearable Textile Electrodes for Portable Electrical Impedance Tomography.

Electrical impedance tomography (EIT), a noninvasive and radiation-free medical imaging technique, has been used for continuous real-time regional lung aeration. However, adhesive electrodes could cause discomfort and increase the risk of skin injury during prolonged measurement. Additionally, the conductive gel between the electrodes and skin could evaporate in long-term usage and deteriorate the signal quality. To address these issues, in this work, textile electrodes integrated with a clothing belt are proposed to achieve EIT lung imaging along with a custom portable EIT system. The simulation and experimental results have verified the validity of the proposed portable EIT system. Furthermore, the imaging results of using the proposed textile electrodes were compared with commercial electrocardiogram electrodes to evaluate their performance.

Electrical Impedance Tomography Analysis Between Two Similar Respiratory System Compliance During Decremetal PEEP Titration in ARDS Patients.

Purpose: The positive end-expiratory pressure (PEEP) level with best respiratory system compliance (Crs) is frequently used for PEEP selection in acute respiratory distress syndrome (ARDS) patients. On occasion, two similar best Crs (where the difference between the Crs of two PEEP levels is < 1 ml/cm H2O) may be identified during decremental PEEP titration. Selecting PEEP under such conditions is challenging. The aim of this study was to provide supplementary rationale for PEEP selection by assessing the global and regional ventilation distributions between two PEEP levels in this situation. Methods: Eight ARDS cases with similar best Crs at two different PEEP levels were analyzed using examination-specific electrical impedance tomography (EIT) measures and airway stress index (SIaw). Five Crs were measured at PEEP values of 25 cm H2O (PEEP25), 20 cm H2O (PEEP20), 15 cm H2O (PEEPH), 11 cm H2O (PEEPI), and 7 cm H2O (PEEPL). The higher PEEP value of the two PEEPs with similar best Crs was designated as PEEPupper, while the lower designated as PEEPlower. Results: PEEPH and PEEPI shared the best Crs in two cases, while similar Crs was found at PEEPI and PEEPL in the remaining six cases. SIaw was higher with PEEPupper as compared to PEEPlower (1.06 ± 0.10 versus 0.99 ± 0.09, p = 0.05). Proportion of lung hyperdistension was significantly higher with PEEPupper than PEEPlower (7.0 ± 5.1% versus 0.3 ± 0.5%, p = 0.0002). In contrast, proportion of recruitable lung collapse was higher with PEEPlower than PEEPupper (18.6 ± 4.4% versus 5.9 ± 3.7%, p < 0.0001). Cyclic alveolar collapse and reopening during tidal breathing was higher at PEEPlower than PEEPupper (34.4 ± 19.3% versus 16.0 ± 9.1%, p = 0.046). The intratidal gas distribution (ITV) index was also significantly higher at PEEPlower than PEEPupper (2.6 ± 1.3 versus 1.8 ± 0.7, p = 0.042). Conclusions: PEEPupper is a rational selection in ARDS cases with two similar best Crs. EIT provides additional information for the selection of PEEP in such circumstances. Supplementary Information: The online version contains supplementary material available at 10.1007/s40846-021-00668-2.

Interference reduction using pattern modulation and demodulation for light-coding depth cameras.

Depth extraction systems with multiple light-coding depth cameras (LCDCs) have been widely used in the field of three-dimensional reconstruction in recent years. However, interference among the depth cameras would significantly deteriorate the quality of depth images and thus limit their efficiency in various applications. In this paper, we first establish the linear illumination model of multiple LCDCs to study the property of interference. Since the interference-free pattern is hard to distinguish from the interfered pattern, we present an interference reduction scheme based on pattern modulation and demodulation to address this problem. Projected patterns from the LCDCs are then uniformly modulated using a coefficient matrix. Afterwards, we further put forward an interference-alignment-based solution to demodulate the captured image frames, thereby fast recovering the interference-free pattern of a single LCDC. Depth images can finally be generated based on the recovered interference-free pattern. Experimental results from simulated and real-world examples show that the proposed scheme can effectively reduce the impact of interference and improve the quality of depth images.

Limited predictability of maximal muscular pressure using the difference between peak airway pressure and positive end-expiratory pressure during proportional assist ventilation (PAV).

BACKGROUND: If the proportional assist ventilation (PAV) level is known, muscular effort can be estimated from the difference between peak airway pressure and positive end-expiratory pressure (PEEP) (ΔP) during PAV. We conjectured that deducing muscle pressure from ΔP may be an interesting method to set PAV, and tested this hypothesis using the oesophageal pressure time product calculation. METHODS: Eleven mechanically ventilated patients with oesophageal pressure monitoring under PAV were enrolled. Patients were randomly assigned to seven assist levels (20-80%, PAV20 means 20% PAV gain) for 15 min. Maximal muscular pressure calculated from oesophageal pressure (Pmus, oes) and from ΔP (Pmus, aw) and inspiratory pressure time product derived from oesophageal pressure (PTPoes) and from ΔP (PTPaw) were determined from the last minute of each level. Pmus, oes and PTPoes with consideration of PEEPi were expressed as Pmus, oes, PEEPi and PTPoes, PEEPi, respectively. Pressure time product was expressed as per minute (PTPoes, PTPoes, PEEPi, PTPaw) and per breath (PTPoes, br, PTPoes, PEEPi, br, PTPaw, br). RESULTS: PAV significantly reduced the breathing effort of patients with increasing PAV gain (PTPoes 214.3 ± 80.0 at PAV20 vs. 83.7 ± 49.3 cmH2O•s/min at PAV80, PTPoes, PEEPi 277.3 ± 96.4 at PAV20 vs. 121.4 ± 71.6 cmH2O•s/min at PAV80, p < 0.0001). Pmus, aw overestimates Pmus, oes for low-gain PAV and underestimates Pmus, oes for moderate-gain to high-gain PAV. An optimal Pmus, aw could be achieved in 91% of cases with PAV60. When the PAV gain was adjusted to Pmus, aw of 5-10 cmH2O, there was a 93% probability of PTPoes <224 cmH2O•s/min and 88% probability of PTPoes, PEEPi < 255 cmH2O•s/min. CONCLUSION: Deducing maximal muscular pressure from ΔP during PAV has limited accuracy. The extrapolated pressure time product from ΔP is usually less than the pressure time product calculated from oesophageal pressure tracing. However, when the PAV gain was adjusted to Pmus, aw of 5-10 cmH2O, there was a 90% probability of PTPoes and PTPoes, PEEPi within acceptable ranges. This information should be considered when applying ΔP to set PAV under various gains.

Electrical impedance tomography monitoring in acute respiratory distress syndrome patients with mechanical ventilation during prolonged positive end-expiratory pressure adjustments.

BACKGROUND/PURPOSE: The time required to reach oxygenation equilibrium after positive end-expiratory pressure (PEEP) adjustments in mechanically ventilated patients with acute respiratory distress syndrome (ARDS) is unclear. We used electrical impedance tomography to elucidate gas distribution and factors related to oxygenation status following PEEP in patients with ARDS. METHODS: Nineteen mechanically ventilated ARDS patients were placed on baseline PEEP (PEEPB) for 1 hour, PEEPB - 4 cmH2O PEEP (PEEPL) for 30 minutes, and PEEPB + 4 cmH2O PEEP (PEEPH) for 1 hour. Tidal volume and respiratory rate were similar. Impedance changes, respiratory parameters, and arterial blood gases were measured at baseline, 5 minutes, and 30 minutes after PEEPL, and 5 minutes, 15 minutes, 30 minutes, and 1 hour after PEEPH. RESULTS: PaO2/fraction of inspired oxygen (P/F ratio) decreased quickly from PEEPB to PEEPL, and stabilized 5 minutes after PEEPL. However the P/F ratio progressively increased from PEEPL to PEEPH, and a significantly higher P/F ratio and end-expiratory lung impedance were found at 60 minutes compared to 5 minutes after PEEPH. The end-expiratory lung impedance level significantly correlated with P/F ratio (p < 0.001). With increasing PEEP, dorsal ventilation significantly increased; however, regional ventilation did not change over time with PEEP level. CONCLUSION: Late improvements in oxygenation following PEEP escalation are probably due to slow recruitment in ventilated ARDS patients. Electrical impedance tomography may be an appropriate tool to assess recruitment and oxygenation status in patients with changes in PEEP.

Is tracheostomy a better choice than translaryngeal intubation for critically ill patients requiring mechanical ventilation for more than 14 days? A comparison of short-term outcomes.

BACKGROUND: Tracheostomy is recommended for patients receiving mechanical ventilation (MV) for 14 days or more in the intensive care unit (ICU). Nevertheless, many patients undergoing prolonged MV remain intubated via the translaryngeal route. The aim of this study was to examine the influence of tracheostomy and persistent translaryngeal intubation on short-term outcomes in patients mechanically ventilated for ≥14 days. METHODS: A retrospective study was conducted using the admissions database of a 75-bed ICU from January 1, 2012, to December 31, 2012. Patients who required prolonged MV without tracheostomy at the time of initiation of a ventilator were included. The outcomes were successful weaning, and ICU and in-hospital death. Cox models were constructed to calculate the influence of tracheostomy on the outcome measures while adjusting for other potentially confounding factors. RESULTS: Of the 508 patients requiring prolonged MV, 164 were tracheostomized after a median 18 days of MV. Patients in whom translaryngeal intubation was maintained had significantly higher ICU (42.7% versus 17.1%, p <0.001) and in-hospital (54.1% versus 22.0%, p <0.001) mortality rates, and a significantly lower successful weaning rate (40.4% versus 68.9%, p <0.001). The results were consistent after matching for the propensity score of performing tracheostomy. Furthermore, a time-dependent covariate Cox model showed that a tracheostomy was independently associated with lower in-hospital mortality (adjusted hazard ratio [aHR], 0.26; 95% confidence interval [CI], 0.18-0.39) and higher successful weaning rate (aHR, 2.05; 95% CI, 1.56-2.68). CONCLUSIONS: Tracheostomy is associated with lower in-hospital mortality and higher successful weaning rates in ICU patients receiving prolonged MV. However, the cost-effectiveness and long-term outcomes of tracheostomy for this cohort require further study.

Morphology, molecular stacking, dynamics and device performance correlations of vacuum-deposited small-molecule organic solar cells.

The "all carbon" organic solar cells (OSCs) based on the homocyclic molecule tetraphenyldibenzoperiflanthene (DBP) as a donor and C60 as an acceptor were comprehensively characterized. The optimized planar-mixed heterojunction device with a DBP:C60 mixture ratio of DBP : C60 (1 : 2) exhibited a power conversion efficiency of 4.47%. To understand why DBP possesses such advantageous characteristics, the correlations of the morphology, molecular stacking, carrier dynamics and performance of DBP:fullerene-based devices have been systematically studied. First, the face-on stacked DBP molecules could enhance both the absorption of light and the charge carrier mobility. Second, DBP : C60 (1 : 2) thin films with optimized domain sizes and partially interconnected acceptor grains led to the most balanced carrier mobility and the lowest bimolecular recombination in devices. Finally, the DBP molecules were found to stack closely using grazing incidence wide-angle X-ray scattering measurements, with a π-π stacking spacing of 4.58 Å, indicating an effective molecular orbital overlap in DBP. The study not only reveals the promising characteristics of DBP as a donor in OSCs but the clear correlations of the thin-film nano-morphology, molecular stacking, carrier mobility and charge recombination found here could also provide insights into the characterization methodology and optimization of the small molecule OSCs.

Teaching Masked Autoencoder With Strong Augmentations.

Masked autoencoder (MAE) has been regarded as a capable self-supervised learner for various downstream tasks. Nevertheless, the model still lacks high-level discriminability, which results in poor linear probing performance. In view of the fact that strong augmentation plays an essential role in contrastive learning, can we capitalize on strong augmentation in MAE? The difficulty originates from the pixel uncertainty caused by strong augmentation that may affect the reconstruction, and thus, directly introducing strong augmentation into MAE often hurts the performance. In this article, we delve into the potential of strong augmented views to enhance MAE while maintaining MAE's advantages. To this end, we propose a simple yet effective masked Siamese autoencoder (MSA) model, which consists of a student branch and a teacher branch. The student branch derives MAE's advanced architecture, and the teacher branch treats the unmasked strong view as an exemplary teacher to impose high-level discrimination onto the student branch. We demonstrate that our MSA can improve the model's spatial perception capability and, therefore, globally favors interimage discrimination. Empirical evidence shows that the model pretrained by MSA provides superior performances across different downstream tasks. Notably, linear probing performance on frozen features extracted from MSA leads to 6.1% gains over MAE on ImageNet-1k. Fine-tuning (FT) the network on VQAv2 task finally achieves 67.4% accuracy, outperforming 1.6% of the supervised method DeiT and 1.2% of MAE. Codes and models are available at https://github.com/KimSoybean/MSA.

ZS-VAT: Learning Unbiased Attribute Knowledge for Zero-Shot Recognition Through Visual Attribute Transformer.

In zero-shot learning (ZSL), attribute knowledge plays a vital role in transferring knowledge from seen classes to unseen classes. However, most existing ZSL methods learn biased attribute knowledge, which usually results in biased attribute prediction and a decline in zero-shot recognition performance. To solve this problem and learn unbiased attribute knowledge, we propose a visual attribute Transformer for zero-shot recognition (ZS-VAT), which is an effective and interpretable Transformer designed specifically for ZSL. In ZS-VAT, we design an attribute-head self-attention (AHSA) that is capable of learning unbiased attribute knowledge. Specifically, each attribute head in AHSA first transforms the local features into attribute-reinforced features and then accumulates the attribute knowledge from all corresponding reinforced features, reducing the mutual influence between attributes and avoiding information loss. AHSA finally preserves unbiased attribute knowledge through attribute embeddings. We also propose an attribute fusion model (AFM) that learns to recover the correct category knowledge from the attribute knowledge. In particular, AFM takes all features from AHSA as input and generates global embeddings. We carried out experiments to demonstrate that the attribute knowledge from AHSA and the category knowledge from AFM are able to assist each other. During the final semantic prediction, we combine the attribute embedding prediction (AEP) and global embedding prediction (GEP). We evaluated the proposed scheme on three benchmark datasets. ZS-VAT outperformed the state-of-the-art generalized ZSL (GZSL) methods on two datasets and achieved competitive results on the other dataset.

EGCN++: A New Fusion Strategy for Ensemble Learning in Skeleton-Based Rehabilitation Exercise Assessment.

Skeleton-based exercise assessment focuses on evaluating the correctness or quality of an exercise performed by a subject. Skeleton data provide two groups of features (i.e., position and orientation), which existing methods have not fully harnessed. We previously proposed an ensemble-based graph convolutional network (EGCN) that considers both position and orientation features to construct a model-based approach. Integrating these types of features achieved better performance than available methods. However, EGCN lacked a fusion strategy across the data, feature, decision, and model levels. In this paper, we present an advanced framework, EGCN++, for rehabilitation exercise assessment. Based on EGCN, a new fusion strategy called MLE-PO is proposed for EGCN++; this technique considers fusion at the data and model levels. We conduct extensive cross-validation experiments and investigate the consistency between machine and human evaluations on three datasets: UI-PRMD, KIMORE, and EHE. Results demonstrate that MLE-PO outperforms other EGCN ensemble strategies and representative baselines. Furthermore, the MLE-PO's model evaluation scores are more quantitatively consistent with clinical evaluations than other ensemble strategies.

Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images.

The task of instance segmentation in remote sensing images, aiming at performing per-pixel labeling of objects at the instance level, is of great importance for various civil applications. Despite previous successes, most existing instance segmentation methods designed for natural images encounter sharp performance degradations when they are directly applied to top-view remote sensing images. Through careful analysis, we observe that the challenges mainly come from the lack of discriminative object features due to severe scale variations, low contrasts, and clustered distributions. In order to address these problems, a novel context aggregation network (CATNet) is proposed to improve the feature extraction process. The proposed model exploits three lightweight plug-and-play modules, namely, dense feature pyramid network (DenseFPN), spatial context pyramid (SCP), and hierarchical region of interest extractor (HRoIE), to aggregate global visual context at feature, spatial, and instance domains, respectively. DenseFPN is a multi-scale feature propagation module that establishes more flexible information flows by adopting interlevel residual connections, cross-level dense connections, and feature reweighting strategy. Leveraging the attention mechanism, SCP further augments the features by aggregating global spatial context into local regions. For each instance, HRoIE adaptively generates RoI features for different downstream tasks. Extensive evaluations of the proposed scheme on iSAID, DIOR, NWPU VHR-10, and HRSID datasets demonstrate that the proposed approach outperforms state-of-the-arts under similar computational costs. Source code and pretrained models are available at https://github.com/yeliudev/CATNet.

Plasma kallistatin levels in patients with severe community-acquired pneumonia.

INTRODUCTION: Community-acquired pneumonia (CAP) requiring intensive care unit (ICU) treatment commonly causes acute respiratory failure with high mortality. Kallistatin, an endogenous tissue kallikrein inhibitor, has been reported to be protective in various human diseases. The aim of this study was to assess the correlations of kallistatin with other biomarkers and to determine whether kallistatin levels have a prognostic value in severe CAP. METHODS: Plasma samples and clinical data were prospectively collected from 54 patients with severe CAP requiring ICU admission. Seventeen healthy control subjects were included for comparison. Plasma kallistatin, kallikrein, and other biomarkers of inflammation (tumor necrosis factor-α (TNF-α), interleukin (IL)-1ß, IL-6, IL-8, C-reactive protein (CRP)), and anti-coagulation (protein C, anti-thrombin III) were measured on days 1 and 4 of ICU admission. Comparison between survivors (n = 41) and nonsurvivors (n = 13) was performed. RESULTS: Plasma kallistatin was significantly consumed in severe CAP patients compared with healthy individuals. Lower day 1 kallistatin levels showed a strong trend toward increased mortality (P = 0.018) and higher day 1 CURB-65 scores (P = 0.004). Plasma kallistatin levels on day 1 of ICU admission were significantly decreased in patients who developed septic shock (P = 0.017) and who had acute respiratory distress syndrome (P = 0.044). In addition, kallistatin levels were positively correlated with anti-thrombin III and protein C and inversely correlated with IL-1ß, IL-6, and CRP levels. In a multivariate logistic regression analysis, higher day 1 CURB-65 scores were independent predictors of mortality (odds ratio = 29.9; P = 0.009). Also, higher day 1 kallistatin levels were independently associated with a decreased risk of death (odds ratio, 0.1) with a nearly significant statistical difference (P = 0.056). Furthermore, we found that a cutoff level of 6.5 µg/ml of day 1 kallistatin determined by receiver operating characteristic curves could be used to distinguish between patients who survived in 60 days and those who did not. CONCLUSIONS: These results suggest that kallistatin may serve as a novel marker for severe CAP prognosis and may be involved in the pathogenesis of CAP through antiinflammatory and anticoagulation effects.

Positive blood culture is not associated with increased mortality in patients with sepsis-induced acute respiratory distress syndrome.

BACKGROUND AND OBJECTIVE: Previous studies have demonstrated that positive blood culture could contribute to poorer outcomes in patients with pneumonia. However, the impact of positive blood culture on the outcomes of patients with sepsis-induced acute respiratory distress syndrome (ARDS) has not been evaluated. METHODS: An observational study that prospectively screened 4861 patients admitted to medical or surgical intensive care units (ICUs) of a tertiary referral centre was performed. RESULTS: Among 4861 admitted patients, 146 diagnosed with sepsis-induced ARDS were enrolled (mean age: 66.1 years). Lower PaO2 /FiO2 , decreased respiratory system compliance, and higher lung injury scores (LIS) on the day of ARDS diagnosis were associated with positive blood cultures (n = 68) rather than negative blood cultures (n = 78). There was no relationship between positive blood culture and in-hospital mortality. Kaplan-Meier estimates also revealed that positive blood culture was not associated with 60-day mortality but with an increased length of stay in the hospital and in the ICU (P = 0.007 and P = 0.016, respectively). Using multivariate logistic regression, higher LIS was independently associated with positive blood culture. In addition, chronic pulmonary disease, lower platelet count, higher LIS, and the development of shock on the diagnosis of ARDS, were independent risk factors for in-hospital mortality. CONCLUSIONS: This study suggests that the presence of positive blood culture is not associated with increased mortality; however, the mean durations of hospital and ICU stays in patients with sepsis-induced ARDS are increased.

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding.

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding.